otsu1

针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)

基于主动学习的高光谱遥感图像分类，主动学习方法也比较流行，新颖。(An Active Learning Approach toHyperspectral Data Classification)

说明：  一种实现超分辨率图像处理的新算法,本算法在综合前人算法的基础上，做了很大改进。很适合图像处理。(An implementation of the new super-resolution image processing algorithms, the algorithm in integrated algorithm based on the predecessors has done a lot of improvements. Very suitable for image processing.)

用于c#获取多个摄像头影像！

说明：  HEBBHEDSIHERMSHERSIHE 可直接运行的直方图增强算法。(HEBBHEDSIHERMSHERSIHE can be directly run by histogram enhancement algorithm.)

经典的跟踪算法，可以对人脸进行实时的跟踪。(Classical tracking algorithm can be real-time face tracking.)

说明：  Matlab代码文字识别：包括图像分割,粘连字符切分,文字识别(Using MATLAB to realize the code of recovering scene view distance map (depth map) from binocular map)

说明：  能够实现对生物芯片的识别和图像处理，识别有效芯片区域，读取阴性和阳性点分布和数量。(It can realize the recognition and image processing of biochip, identify the effective chip area, read the distribution and number of negative and positive points.)

说明：  图像融合算法，exercise1:为基于小波变化与局部能量的图像融合方法 exercise2:为基于自适应算法的图像融合算法 myfunction1~myfunction5:为性能指标评价函数(Image fusion algorithm, exercise1: wavelet-based change and the local energy method of image fusion exercise2: adaptive algorithm based on image fusion algorithm myfunction1 ~ myfunction5: performance indicators for the evaluation function)

基于OPCV的blob搜索算法，图像处理经典算法(OPCV the blob-based search algorithm, image processing, the classical algorithm)